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  <div class="headertitle"><div class="title">SubrangeKernelTutorial.cpp</div></div>
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<a href="_subrange_kernel_tutorial_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="preprocessor">#include &lt;<a class="code" href="_normalize_kernel_unit_variance_8h.html">shark/Algorithms/Trainers/NormalizeKernelUnitVariance.h</a>&gt;</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="preprocessor">#include &lt;<a class="code" href="_gaussian_rbf_kernel_8h.html">shark/Models/Kernels/GaussianRbfKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="preprocessor">#include &lt;<a class="code" href="_weighted_sum_kernel_8h.html">shark/Models/Kernels/WeightedSumKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="preprocessor">#include &lt;<a class="code" href="_polynomial_kernel_8h.html">shark/Models/Kernels/PolynomialKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="preprocessor">#include &lt;<a class="code" href="_linear_kernel_8h.html">shark/Models/Kernels/LinearKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &lt;<a class="code" href="_subrange_kernel_8h.html">shark/Models/Kernels/SubrangeKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;<a class="code" href="_data_distribution_8h.html">shark/Data/DataDistribution.h</a>&gt;</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &lt;<a class="code" href="_random_8h.html">shark/Core/Random.h</a>&gt;</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span> </div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="keyword">using namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>;</div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span> </div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment">//our problem</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="keyword">class </span>UniformPoints : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_data_distribution.html" title="A DataDistribution defines an unsupervised learning problem.">DataDistribution</a>&lt;RealVector&gt;</div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span>{</div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    UniformPoints(std::size_t dimensions){</div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>        m_dimensions = dimensions;</div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    }</div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span> </div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_data_distribution.html#a3358c43ba05299220ff8df6d5ea7bc75" title="Generates a single pair of input and label.">draw</a>(RealVector&amp; input)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        input.resize(m_dimensions);</div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>        <span class="keywordflow">for</span> ( std::size_t j=0; j&lt;m_dimensions; j++ ) {</div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>            input(j) = <a class="code hl_function" href="namespaceshark_1_1random.html#a18f302ea18f70835c59935973ba8ea84" title="Draws a number uniformly in [lower,upper] by drawing random numbers from rng.">random::uni</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, -1,1);</div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>        }</div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>    }</div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span> </div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    std::size_t m_dimensions;</div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>};</div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span> </div>
<div class="foldopen" id="foldopen00031" data-start="{" data-end="}">
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"><a class="line" href="_subrange_kernel_tutorial_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">   31</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="_subrange_kernel_tutorial_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>()</div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>{</div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>    std::cout &lt;&lt; <span class="stringliteral">&quot;\n ----- Starting MklKernel normalization demo ---- \n\n&quot;</span> &lt;&lt; std::flush;</div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>    std::size_t num_dims = 9;</div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>    std::size_t num_points = 200;</div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>    UniformPoints problem(num_dims);</div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>    <a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> data = problem.generateDataset(num_points);</div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>    </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>    <a class="code hl_class" href="classshark_1_1_gaussian_rbf_kernel.html" title="Gaussian radial basis function kernel.">DenseRbfKernel</a>        basekernel1(0.1);</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>    <a class="code hl_class" href="classshark_1_1_linear_kernel.html" title="Linear Kernel, parameter free.">DenseLinearKernel</a>      basekernel2;</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>    <a class="code hl_class" href="classshark_1_1_polynomial_kernel.html" title="Polynomial kernel.">DensePolynomialKernel</a>  basekernel3(2, 1.0);</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>    </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    std::vector&lt; AbstractKernelFunction&lt;RealVector&gt; * &gt; kernels;</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>    kernels.push_back(&amp;basekernel1);</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    kernels.push_back(&amp;basekernel2);</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    kernels.push_back(&amp;basekernel3);</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>    </div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    std::vector&lt; std::pair&lt; std::size_t, std::size_t &gt; &gt; frs;</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    frs.push_back( std::make_pair( 0,3 ) );</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    frs.push_back( std::make_pair( 3,6 ) );</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>    frs.push_back( std::make_pair( 6,9 ) );</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>    </div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    <a class="code hl_class" href="classshark_1_1_subrange_kernel.html" title="Weighted sum of kernel functions.">DenseSubrangeKernel</a> kernel( kernels, frs );</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    <a class="code hl_class" href="classshark_1_1_scaled_kernel.html" title="Scaled version of a kernel function.">DenseScaledKernel</a> scale( &amp;kernel );</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    </div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    <a class="code hl_class" href="classshark_1_1_normalize_kernel_unit_variance.html" title="Determine the scaling factor of a ScaledKernel so that it has unit variance in feature space one on a...">NormalizeKernelUnitVariance&lt;&gt;</a> normalizer;</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    normalizer.<a class="code hl_function" href="classshark_1_1_normalize_kernel_unit_variance.html#a4bdc12938183edab9acdfd89bde72286">train</a>( scale, data );</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    std::cout &lt;&lt; <span class="stringliteral">&quot;    Done training. Factor is &quot;</span> &lt;&lt; scale.<a class="code hl_function" href="classshark_1_1_scaled_kernel.html#ab039d0f8d3e1e40d61d0897f1c4953a4">factor</a>() &lt;&lt; std::endl;</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    std::cout &lt;&lt; <span class="stringliteral">&quot;    Mean                   = &quot;</span> &lt;&lt; normalizer.<a class="code hl_function" href="classshark_1_1_normalize_kernel_unit_variance.html#aa7dbe18d207e4fd219bb27dd982c699e">mean</a>() &lt;&lt; std::endl;</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    std::cout &lt;&lt; <span class="stringliteral">&quot;    Trace                  = &quot;</span> &lt;&lt; normalizer.<a class="code hl_function" href="classshark_1_1_normalize_kernel_unit_variance.html#a4be3973e544871a8e7bdf1f77d4670fb">trace</a>() &lt;&lt; std::endl &lt;&lt; std::endl;</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    <span class="comment">//check in feature space</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    <span class="keywordtype">double</span> control = 0.0;</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    <span class="keywordflow">for</span> ( std::size_t i=0; i&lt;num_points; i++ ) {</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        control += scale.<a class="code hl_function" href="classshark_1_1_scaled_kernel.html#afc59d0d0d69706360cfd5b30bbd10a0e">eval</a>(data.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(i), data.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(i));</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        <span class="keywordflow">for</span> ( std::size_t j=0; j&lt;num_points; j++ ) {</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>            control -= scale.<a class="code hl_function" href="classshark_1_1_scaled_kernel.html#afc59d0d0d69706360cfd5b30bbd10a0e">eval</a>(data.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(i), data.<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(j)) / num_points;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        }</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    }</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    control /= num_points;</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    std::cout &lt;&lt; <span class="stringliteral">&quot;    Variance of scaled MklKernel: &quot;</span> &lt;&lt; control &lt;&lt; std::endl;</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>}</div>
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